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Category: Analytics

How to forecast high-profit low-volume products in supply chain management and analytics

Posted on by Imarticus Learning

Supply chain management is a broad term that has many applications. Supply chain managers are responsible for ensuring that the right products get to the right place at the right time and that they have all of their necessary components. Supply chain analytics can be used in almost any industry, but it is especially important in industries where high-profit low-volume products exist – such as fashion retail. Hence, a supply chain management career is in high demand.

The Growing Importance of SCM Analytics?

Supply chain management and analytics are becoming more important than ever. Supply chains are growing in size, importance, and complexity – especially as modern businesses expand into global markets. Supply chains have become so complex that managers can no longer rely on traditional methods of forecasting such as historical data or gut intuition! Instead, they must develop a new approach to forecasting high-profit low-volume products using supply chain analytics!

How to forecast high-profit products in supply chain management and analytics?

  • As Supply Chain Management continues to be a growing field for professionals, the relationships between companies and their suppliers continue to grow as well. Over the past few years, there has been an increase in focus on analytics within supply chain management as it can help provide better insight into business decisions that need to be made. 
  • When considering forecasting high-profit low volume products with Supply Chain Analytics, certain tools may come in handy. With the Logistics and supply chain management course, software programs, and analytic tools available today, Supply Chain managers would have more opportunities than ever before when trying to forecast demand for future products they will sell. 

     

  • Using analytical methods like linear regression analysis can also offer helpful techniques on how best to predict demand for certain products. This can be used when Supply Chain Management professionals want to forecast demand for different types of products, especially those with low volume and high-profit margins. 
  • Other methods Supply Chain Managers can use are multi-variate regression analysis as well as decision trees which may help them make better business decisions in the future concerning their supply chain management processes. 
  • With a greater emphasis on Supply Chain Analytics combined with effective forecasting techniques, Supply Chain Managers will have more opportunities than ever before to offer customers an improved experience throughout their buying process. 
  • Using these tools effectively would also increase sales revenue from new product offerings since having access to this type of data is becoming increasingly important among modern businesses today. 
  • In conclusion, Supply Chain Managers can use Supply Chain Analytics, Econometric Forecasting Software, and Statistical Modeling Tools to effectively forecast high-profit low volume products every day. This allows them to increase their sales revenue from new product offerings as well as offer customers a better experience throughout the buying process.

Make Your Career in Supply Chain Management and Analytics with Imarticus Learning

Imarticus Learning offers a Supply chain Management course to build the career of supply chain aspirants. The duration of the course is 6 months. It is uniquely designed by IIT faculty and industry leaders to help you learn and make a bright career. With the ever-increasing trend of e-commerce, the amount of movement of goods has been ever-increasing. There has been a disproportionate jump in the number of jobs for SCM across industries.

Here’s Course USPs:

  • Experiential learning & impressive project portfolio
  • Cutting-edge curriculum and certification from e-learning center, IIT Roorkee
  • Real-life industry project-based learning for a better know-how.

For further details, contact us through the Live Chat Support system or visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon.

What’s happened to the data analytics job market in the past year?

Posted on by Imarticus Learning

A data scientist has been one of the topmost jobs people have been trying to land for a long time. And well after witnessing the benefits of data science and analytics in literally every sector, there is no wonder why. It helps in fields like education, retail, customer service, the health sector, and tourism. It helps corporate firms where it matters. That is, in processing, analyzing, managing, and storing a vast amount of data.

It also helps them to make predictions according to the changing market trends and client demands. This is why it is important to learn data analytics if you want to pursue a career as a data analyst. 

A lot of institutions offer good data analytics courses in India. Check out Imarticus Learnings’ data analytics certification course to hone your skills properly. This will provide you with enough exposure and real-life experience which, in turn, will help you land your dream data analytics job. 

However, last year saw the data analytics job falling behind in the charts for the first time. Now, is it finally coming down from its throne, or is it just another victim of the coronavirus? That is what we are trying to figure out here. Keep reading to learn more.

Is the market decreasing or a victim of Covid-19?

2020 saw a lot of upheavals globally. From educational institutions being shut down to corporate offices going on hiatus for months and some small businesses going out of business altogether, it was a year of getting used to the new normal. With that came the trend and the necessity to work from home.

Not to mention the terrible loss people faced all over the world. Unfortunately, with the new variant on the rise once again, the troubles seem far from over as of now. This also caused a lot of people out of jobs overnight. Not only that, but a lot of jobs went out of practice as well. 

People are still figuring out how to cope with this unprecedented situation. So, as of now, it is really up for debate as to what caused this upheaval in the hierarchy of job positions. Some things come into play though when it comes to changing market trends. Let us look at the situation by trying to analyze those.

Economic factors that factor into changing trends

About three major factors disrupt an ongoing situation, especially in the job market. Those are, as follows:

  • Demand: The reason why any job ranks as the topmost is its demand. Thankfully, the demand for a data analytics job is still very high, as it still ranks as number three on the list. So, the era of data science is far from over.
  • Supply: The supply of data scientists is quite low as of now. And, it seems that it is going to stay that way for years, so the job is going to keep reigning over for a long time.
  • Growth: Growth is a major factor when it comes to any job being relevant. And, the market for data scientists is still growing. In fact, if reports are to be believed, then this field saw an increase of about 650% since 2012. So, it is safe to say that the market will remain relevant in the coming years.

Conclusion

To begin your career as a data analyst, you need to learn from the best. Check out Imarticus Learnings’ data analytics course and boost your career to the max. 

Here’s what happens when you master the concepts in artificial intelligence

Posted on by Imarticus Learning

Artificial Intelligence is the ability of machines to take action or make decisions on their own without human supervision. AI fundamentally tries to emulate the intelligent behavior of human beings and handles tasks similarly, if not in a more efficient manner.

Artificial Intelligence is also attributed to being faster and being unbiased (unless training data is biased). AI, Deep Learning, and Machine Learning power a lot of services and products we use in our day-to-day life.

Implementations of AI

Here are some implementations of AI:

  • Autonomous Vehicles: Autonomous Vehicles such as Teslas are AI-driven and are able to avoid collisions and navigate around with ease with the help of various sensors such as LiDAR and cameras.

  • Robots and Drones: Robots and Drones are becoming autonomous with the help of AI and now do not require human supervision or a human remotely controlling these machines.

  • Chatbots: Chatbots are smart response systems that are great implementations of AI-powered by NLP or Natural Language Processing. Chatbots are becoming smarter and cannot be distinguished from real human beings soon.

  • Virtual Assistant or Voice Assistants: Virtual Assistants and Voice Assistants such as Cortana, Siri, or Google Assistant are all powered by AI and learn from our actions as well as data from users worldwide to make our digital experience better or to carry out tasks for us better.

  • Sentiment Analytics: Sentiment Analytics use AI that is trained with the help of data that has been labeled with positive, negative, or any other custom sentiments. With the help of this and NLP, the software is able to determine the sentiment behind textual data, social media posts, or content.

  • Search Engines: Search Engines such as Google and Yahoo are powered by AI as well to make searching for things easier and fetch the most relevant results. The AI models in Search Engines are trained to fetch related results as well.

  • Smart Homes: Smart Homes used IoT (Internet of Things) devices and various sensors in devices such as phones and watches in order to provide homeowners with a better experience or a customized experience. For instance, setting the right temperature when the owner returns home or turning on specific lights when the user goes into a room. These smart homes can also be customized directly through mobile devices but owners can also decide to let them act autonomously.

  • Predictive Texts and Spell-check: Predictive texts are spell-checking features that are also powered by AI that is trained using NLP models. These systems are added to software or devices to automatically detect grammatical errors or identify spelling mistakes. Devices, applications, and even services such as Gmail can now even predict the next thing you are about to say and offer suggestions to make one’s job easier.

  • Media Recommendation Systems: Media recommendation systems are implementations of Machine Learning that powers services such as Netflix, Spotify, Youtube, and others. These AI implementations use a user’s video or audio history data and then suggest other media that the user might enjoy.

  • Production and Manufacturing Automation Systems: AI empowers the automation of production and manufacturing. BPA or Business Process Automation helps in reducing cost and AI-backed machines help in making manufacturing more efficient than human workers.

How Mastering AI Helps You

Mastering AI can help one get very desirable job roles in MNCs such as Microsoft, Amazon, Google, or Netflix. One can learn AI topics with courses such as the Artificial Intelligence course in E&ICT Academy, IIT. The Artificial Intelligence course in E&ICT Academy, IIT is a great way to start your career in AI.

How is RPA impacting the supply chain management and analytics industry?

Posted on by Imarticus Learning

RPA stands for Robotic Process Automation, which is a new system used in supply chain management systems. RPA automates those processes that are otherwise operated manually. This reduces errors and anomalies drastically. It allows the companies to utilize their employees for actual brainstorming rather than correcting the various issues on a regular basis. 

RPA has had a major impact on the daily operations of a supply chain system with its productivity increasing many folds in recent times. If you intend to choose a supply chain management course with analytics, having a general idea about its impact will be beneficial. 

Pros and Cons of RPA in the SCM industry

It has not been long since RPA was used in SCM so it may not be time to judge it to be a good or bad move. But it has been in use for a while to see what are its advantages and drawbacks.

Benefits of RPA in SCM

  • Order processing and payment

Processing the orders and tracking the payments are some of the most difficult tasks in the supply chain system. But automating both of these, companies can save time and effort while making it simple for the customers or the company to keep track. The automation includes timely processing of the orders and sending out bills through emails and text messages. 

  • Communications

It is important to keep track of the processes and inform the involved parties about the progress or delays concerning the shipments, etc. The automated email system sends out emails whenever an order is placed, the product is shipped, or out for delivery. Such automation makes the system transparent and reliable. 

  • Inventory management

Every certificate course in supply chain management teaches that this is the most important department of the supply chain. Inventory management is a major part of the supply chain and by automating this department, companies can ensure the balance of supply and demand. The automated system can send notifications for low levels of stock and reordering processes. It could also use historical data to predict the inventory levels depending on the demand. 

  • Shipment status

 Communication of the shipment status could be completely automated right from the opening line of the email to assessing what the customer expects from such communication. Sending out the regular updates of the shipment is one such example and it will require minimal human intervention and only on some rare occasions.

  • Supply and demand planning

RPA helps gather, compile, analyze, and present the data for the regular planning for supply according to the demand. By using AI and ML, reduces common human errors and makes the system more efficient. 

Drawbacks of RPA in SCM

Rather than considering it as a drawback it would be better seen as a challenge that could have a solution in the near future. The common issues with RPA in SCM are 

  • Standardizing the processes even with proper documentation
  • Need for constant IT support
  • Keeping up with the expectations of stakeholders and gaining their trust to implement the RPA system
  • Engaging the employees and making them accept the system 

Conclusion

The world is moving forward with technological advancements so every industry must keep up with these changes. The SCM system requires professionals having such advanced skills and that is the reason why one should opt for the supply chain management online course such as the Professional Certification In Supply Chain Management & Analytics at Imarticus.

It will help you be a supply chain manager who has a thorough knowledge of the latest developments in this system. 

5 NLP techniques every data scientist should know

Posted on by Imarticus Learning

Have you ever wanted to master NLP? If so, I have five techniques that will change your life! In the last few decades, computers able to understand and process natural language. As a result, many new applications can leverage this technology for more accurate processing of text data.

One of these is Natural Language Processing (NLP). NLP has become an essential part of our lives as it allows us to talk with machines in a way they understand. This blog post will discuss five NLP techniques every data scientist should know. 

1) Tokenization: 

  • A technique that breaks up sentences into individual words or word tokens. 
  • It is the first step in text processing as it gives us a way to deal with each word individually. 
  • Tokenization is either done by splitting up an input string into words or groups of the word. Depending on the application, you might choose one over the other. 
  • For example, splitting words would be the best approach to find new misspelled versions of a known word. 

2) Stemming: 

  • Stemming is a method that reduces words to their root. It allows us to deal with variations of a comment by using its root form instead. 
  • For example, “running,” “runs,” and “ran” would all be reduced to the stem word “run.” Stemming algorithms share the same purpose: to remove the grammatical additions of words to get their root form. 
  • It allows for automatic text simplification, which is essential when condensing the input data into a single searchable string.

3) Lemmatization: 

  • Lemmatization is a process that reduces inflected words to their base or dictionary form. 
  • For example, reduction of “walked,” “walking,” and “walk” to the root word walk.
  • Lemmatization is stemming done right. Stemming reduces words to their root forms, but it does not take into account morphological rules. On the other hand, Lemmatization builds up word knowledge, which allows for base or uninflected word matching.

4) Keywords Extraction: 

  • This process finds the most important words when applied to text, phrases, or sentences. 
  • Keywords extraction means finding essential words in a given sentence, and this gets done by using TF-IDF (Term Frequency-Inverse Document Frequency).

5) Sentimental Analysis: 

  • Sentiment analysis is a text mining technique that has applications in many fields. 
  • It can also be helpful when building chatbots as word sentiment can give us an idea of what the user is saying. 
  • Sentimental Analysis helps identify emotional, social, or opinionated aspects within written language.

Explore and Learn Data Science with Imarticus Learning

Our Data Science course details include Capstone Initiatives, real-world business projects, relevant case studies, and mentorship from industry leaders who matter to help students become experienced Data Scientists.

Some course USP:

  • This data science course in India aid the students in learning job-relevant skills.
  • Impress employers & showcase skills with the certification of data science endorsed by India’s most prestigious academic collaborations.
  • World-Class Academic Professors to learn from through live online sessions and discussions.

Contact us through the chat support system or visit Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon training centers.

Why embodied learning is essential to careers in artificial intelligence

Posted on by Imarticus Learning

Technology is now instrumental in every business process and artificial intelligence is the newest tool that companies are trying to implement. Therefore, there is a vast scope for jobs in the field. However, for a career in artificial intelligence, you need to invest in learning about the discipline. Embodied learning or learning that involves both the body and the mind, is crucial in an artificial intelligence course.

This is because the field of artificial intelligence is developed by closely observing and replicating human behavior. If you are looking for such a program that will help you focus on a career, you can choose Imarticus Learning’s AIML course. This course involves the best learning methods and an industry-oriented curriculum to provide the necessary training. 

How Can Embodied Learning Help in an Artificial Intelligence Career? 

If you wish to learn AI and establish a career in that field, you need to invest in the process of embodied learning. This is particularly because embodied artificial intelligence is developing quickly. According to Linda Smith’s 2005 hypothesis, intelligence is a reaction to or a product of the sensorimotor activity, and it is born out of the interaction between the environment and an agent.

While this is true in terms of human intelligence and cognitive function, it is also true for artificial intelligence, which at the most basic level mimics human behavior in a faster and more error-free space. 

Embodied learning is crucial for artificial intelligence because it helps to focus on data or metrics that are generated from a human perspective. Thus, there is a greater chance for that data to be accurate once it is implemented to optimize various processes.

When you participate in embodied learning, you are able to appreciate where artificial intelligence draws from and why it is essential to understand human cognition. Once you become a professional in the field, this same training will prepare you to combine computer vision, Internet AI, and Natural Language Processing to generate outcomes that are more closely related to human patterns. Such AI solutions will therefore have more potential to positively impact the business processes. 

Why is Imarticus Learning a Good Choice for a Career in Artificial Intelligence? 

To participate in embodied learning and to have a better understanding of what embodied artificial intelligence is, you can opt for Imarticus Learning’s certificate course in Artificial Intelligence and Machine Learning. This AI certification program is for students who have completed their Bachelor’s or Master’s in statistics, mathematics, economics, computer science, engineering, or science and have a minimum of 50% in graduation.

If you are eligible you can enroll in our Artificial Intelligence and Machine Learning certificate course. The mode of learning for this training is online and it is done through live lectures so that you can learn, interact and build contacts with academicians and industry professionals.

We have collaborated with the E&ICT Academy and IIT Guwahati to create the course curriculum. Therefore, you will be learning from the best academicians in the field and they will be able to give you a holistic education in embodied artificial intelligence.

You will also be receiving industry certification which will prepare you for interviews with renowned companies in the industry. We at Imarticus Learning ensure hands-on training and experience for all our students. Once you enroll in the Artificial Intelligence and Machine Learning course, you will be able to sit for live lectures every week.

The lectures are held for 8 hours each week and you can interact with your teachers, guest lecturers, and peers. Such interactions will help you develop a complete understanding of embodied learning and its implementation in the field of artificial intelligence.  

The practical training and experience portion of our program is offered through project work and assignments. You will get to participate in 25 industry-related projects and focus on assignments that deal with real-world issues. This will prepare you for the current industry and help you become the best potential employee possible.

Best practices to set up safety stock targets in supply chain management and analytics

Posted on by Imarticus Learning

In the modern era, technology has been an indispensable part of our lives. And the fact remains constant for various industries including the field of supply chain management. With cloud computing, big data, and other forms of advanced analytics, businesses are becoming more efficient than before with their supply chains. But what exactly does this mean? 

Supply chain management is a management process that focuses on the smooth flow of information, materials, and services to meet customer demand. It is a complex process that requires the coordination of many different players, from suppliers to distributors and customers.

Now that we’ve discussed supply chain management, it’s time to learn about safety stock. Safety stocks are a component of supply chain management aimed at preventing stockouts. 

The term “stockout” refers to a situation in which a firm has run out of inventory, leaving clients without a product or service. This can be quite harmful to businesses because it could result in lost revenue and customer satisfaction. Therefore, it is clear that Safety stock targets are one of the most important aspects of supply chain management.

Essential elements of setting up safety stock goals in supply chain management and analytics:

  1. Using advanced analytics can help ensure that your company will never run out of inventory and prevent the loss in revenue that comes with a stockout. Analytics can also help you to avoid overstock, which occurs when a company has more inventory than they need. Apart from this, Advanced analytics can help you to determine the optimal amount of inventory that should be kept in a warehouse.

  2. In order to ensure your business is using safety stocks effectively, it’s important that all stakeholders work together from the beginning stages of an operation plan. This will allow companies to make decisions based on accurate data and demand forecasts for their products.

  3. It is also important to take into consideration the various planning horizons when setting up safety stock targets. For example, if your plan has a long-term horizon (more than one year), you will want to make sure that the inventory accounts for fluctuations in demand over time, however, if your supply chain management strategy focuses on short-term goals. You might want to have a more streamlined strategy that focuses primarily on current demand.

  4. You may also enroll in a supply chain management course to learn more about the subject and how it might assist your organization to operate successfully.

Explore SCM with Imarticus Learning:

Imarticus learning offers you a Supply chain management course with analytics that will provide you with the skills and knowledge to understand supply chain planning, management, and optimization. You will get to learn about the modern supply chain management concepts and how to apply them in your specific business environment.

This course contains numerous case studies which will help you understand exactly how these issues are handled by professionals working in Supply Chain Management across various industry sectors. Also, this course will take your Supply chain management career to the next level. 

Course USP’s:

  • Real SCM projects and case studies.
  • Industry expert faculty will help you apply SCM concepts in your organization.
  • Exclusive Videos and Podcasts by industry leaders to take your career ahead.
  • Offers you a certification from one of the most renowned supply chain management training institutes.

Careers in artificial intelligence and machine learning

Posted on by Imarticus Learning

Customer experience is crucial for business growth, and through the omnichannel or a multi-channel approach it is possible to generate better revenue. If you are interested in the impact of technological solutions in sales and marketing, you can learn artificial intelligence. This will help you prepare for an exciting and rewarding career. You can choose the AIML course from Imarticus Learning. 

Top Benefits of Artificial Intelligence in Omnichannel

If you are considering a career in artificial intelligence, you can work in the sales and marketing industry. To know the impact of artificial intelligence on omnichannel, take a look at the points below. 

 

  • Scaling Customer Experience 

 

Artificial intelligence and machine learning can be used to effectively restructure the entire IT architecture of companies. This helps to set a scale for the improvement of customer experiences by analyzing all customer preferences. 

 

  • Trial Features on Mobile Apps

 

If you are able to combine artificial intelligence with machine learning, you can ensure that mobile apps for businesses offer a better experience to potential buyers. For example, if you are working for a company that sells clothes, you can create a feature on the mobile app that allows customers to try on the garments. Such trial features are possible for any products that need to be worn or applied. 

 

  • Creation of Accurate Buyer Personas

 

Buyer personas are essential to creating a good marketing strategy for omnichannel. You can implement artificial intelligence to create buyer personas that are accurate and will help to design products and services that cater to that target audience. 

 

  • Use of Propensity Models

 

Machine learning algorithms and artificial intelligence can be applied to the creation and use of propensity models for predictive analysis. These help to determine customer responses to price bundling, offers, email advertisements and other call-to-action methods. 

 

  • Increase in Operational Efficiency

 

Artificial intelligence can be used in every aspect of an omnichannel and it will assist in developing and revolutionizing customer support. In doing this, artificial intelligence can improve the operational efficiency of a business. 

 

  • Quick Analysis of Customer Behaviour

 

Using artificial intelligence and machine learning, you can analyze customer behavior or the response of potential customers to a certain product or service. The insights that you obtain from the analysis will help gain better responses and thus improve the sales close rates. 

 

  • Increase of Revenue

 

As artificial intelligence for the omnichannel helps to improve operational efficiency, it can also help to generate better revenue. Since every product or service is tailored to fit customer preferences, the revenue is bound to increase. 

 

  • Improved Traceability of Orders

 

Artificial intelligence can be used to track different orders across various channels. If operational risks in the channels are reduced, the traceability of orders will improve and it will have a positive impact on customer experience. 

 

  • Better Marketing Strategies

 

Marketing strategies for the omnichannel need to be optimized to understand what is working and what is not working for the business. Artificial intelligence in marketing for an omnichannel can help in the prioritization of sales time and sales strategies, and the improvement of customer profiles. 

Study Artificial Intelligence from Imarticus Learning

If you want a career in implementing artificial intelligence for omnichannel, you should opt for an artificial intelligence course. At Imarticus Learning we offer the best certification in Artificial Intelligence & Machine Learning program. The course is ideal for aspiring data scientists and analysts. You can also opt to pursue a career in machine learning engineering once you complete this certificate course. To create the curriculum and provide certification, we collaborated with the country’s top institutions, IIT Guwahati and the E&ICT Academy. 

At Imarticus Learning, you will be able to participate in live lectures that are held for 8 hours every week. This will help you build important networks and interact with industry professionals, making it easier to land lucrative jobs.

Should we reconcile forecasts to align supply chain management and analytics?

Posted on by Imarticus Learning

One of the biggest perks of analytics in supply chain management is its utilisation of the demand forecast. Managers at different levels of the supply chain may be using it for different purposes. The common factor is that they all use the data for making some sort of decision.

One could say that the supply chain management career will require you to make a lot of decisions based on the various data available at any given time. Decisions of different levels may be based on the same set of data or entirely different ones as well. 

One must think about whether or not to align multiple levels using the forecasts. So the question here is, will it be wise to use a unified forecast across all levels?

Unified forecast in Supply Chain Management (SCM)

The technological advancements have made it easier to unify the various sections or levels in the SCM and send a common forecast for all. But, it is the only easy path here as there are more challenges ahead for such a decision to be successful. The main challenges here are, 

  • Efficiency: It is going to be a tedious process to repeat the same process every month for the specified period.  
  • Optimality: The optimal model that is successful for one product or material may not be at the same level for another product, even for the same company. 
  • Alignment: Aligning the forecast and aligning the performance may not be the same. Even with a unified forecast, the outcome of different departments may be different so it is not advisable to take such a move. 

So, the answer to the question is, it is not advisable to align the forecast in a supply chain system. But what you can do is to provide the data in a single platform where it is accessible for all levels. This way a lot of fragmentation could be prevented. This is where a skilled supply chain manager comes into play. 

Importance of Supply Chain Management & Analytics

These days businesses rely on data to come with better plans for their future. The supply chain uses the massive data generated through its operations on a daily basis. The SCM system and analytics can use this data to predict future trends so that the companies can optimize their production or increase their sales. 

Opting for a supply chain management course with analytics will give you a clear idea about the various processes involved in these operations. The designing of the distribution network, planning, and coordination, etc are some of the topics that are included in such courses. 

The Professional certification in Supply Chain Management & Analytics course here at Imarticus is in collaboration with IIT Roorkee. The course offers live sessions, discussions, assignments, assessments, and a capstone project that will award a certificate after successful completion. You will receive career support with the guidance of experts. The career options ahead include the Supply Planning Analyst, Procurement Specialist, Supply And Operations Planner, Quality Assurance Manager, Logistics Manager, etc. 

Conclusion

Completing a course in SCM will enable you to see and make appropriate decisions based on the analytics. A smart manager will be able to run the show for any given period and will be able to adjust the decisions at crucial points, without compromising the competitive advantage. It is all about balancing the supply and demand so that neither of these is above or below the other. 

What 60% of data analytics learners do wrong

Posted on by Imarticus Learning

Data science is a field that is as demanding as it is difficult. It has become a necessary part of our lives. Whether managing education, retail or corporate, data analytics has come in really handy in recent years. Corporate especially is a field where data analytics helps a lot as there are always big amounts of data to be processed. It is in no way an easy job. The job market is also very demanding, but thankfully numerous positions are being offered across the globe. 

This is why if you are thinking of switching to a data analytics career, then you should learn data analytics properly. Fortunately, a lot of institutions in India offer compact courses on it. Such an institution is Imarticus Learnings who offer a solid data analytics certification course with placements. This will not only cover the basics of ‘what does a data analyst do’ but also hone your skills to a different level. Now, here, we are going to elaborate on some primary mistakes that a majority of data analytics learners do wrong to help you avoid them altogether. Please read on to learn more.

What does a data analyst do?

A data analyst needs to process big data, including the current trends of a market, the inefficiencies present in the current system of a company, changing market trends, changes in customer demands, and so on very quickly. This is the only way to analyze certain problems and address them accordingly. Data analysts need to make suggestions for a more profitable approach for the company that they are in. They also need to collaborate with other departments to make a plan that works for all and even supervise it regularly. So, mistakes are not appreciated.

The mistakes to avoid

There are some primary mistakes that beginners end up making that can become harmful to their careers. They are, as follows:

  • Jumping into things headfirst: You need to analyze the problem first properly before jumping into conclusive solutions. The best way to deal with this is to scope the entire value of delivery from the get-go. This comes in really handy later as it gives a clear value of what data science can bring with each step.
  • Exploratory Data Analysis (EDA) is a must: Although EDA might seem like a tedious aspect, it is a must. It gives you the edge in both competitions and real-life projects. Skipping it entirely and jumping straight into modeling can turn out to be a real problem later on.
  • Spend time on feature engineering: This is directly linked to your building models. You need to spend enough time building predictive parameters after the initial processing and cleaning of a data set. Although directly jumping to grid searches and model building without this might work in some cases, that does not work well when you are trying to build a proper score.
  • Global models are part of the process: It is necessary to have the entire picture in mind before getting into projects seriously. This will help you make a plan with minimum efficiency and easier structures if the client has limited resources.

 

  • You also need to talk to domain experts regularly as they can provide insights you might overlook sometimes.
  • Know the basics properly.
  • Improve your connections.

Conclusion

The job can seem intimidating at first, but there are also some seriously interesting aspects to it. For a better understanding, learn data analytics with Imarticus Learnings’ data analytics certification course to give your career the boost it needs.